Approaches on crowd counting and density estimation: a review
In recent years, urgent needs for counting crowds and vehicles have greatly promoted
research of crowd counting and density estimation. Benefiting from the rapid development of …
research of crowd counting and density estimation. Benefiting from the rapid development of …
Distribution matching for crowd counting
In crowd counting, each training image contains multiple people, where each person is
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …
A generalized loss function for crowd counting and localization
Previous work shows that a better density map representation can improve the performance
of crowd counting. In this paper, we investigate learning the density map representation …
of crowd counting. In this paper, we investigate learning the density map representation …
NWPU-crowd: A large-scale benchmark for crowd counting and localization
In the last decade, crowd counting and localization attract much attention of researchers due
to its wide-spread applications, including crowd monitoring, public safety, space design, etc …
to its wide-spread applications, including crowd monitoring, public safety, space design, etc …
Transcrowd: weakly-supervised crowd counting with transformers
The mainstream crowd counting methods usually utilize the convolution neural network
(CNN) to regress a density map, requiring point-level annotations. However, annotating …
(CNN) to regress a density map, requiring point-level annotations. However, annotating …
Jhu-crowd++: Large-scale crowd counting dataset and a benchmark method
We introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++)
that contains “4,372” images with “1.51 million” annotations. In comparison to existing …
that contains “4,372” images with “1.51 million” annotations. In comparison to existing …
Spatial uncertainty-aware semi-supervised crowd counting
Semi-supervised approaches for crowd counting attract attention, as the fully supervised
paradigm is expensive and laborious due to its request for a large number of images of …
paradigm is expensive and laborious due to its request for a large number of images of …
Cnn-based density estimation and crowd counting: A survey
Accurately estimating the number of objects in a single image is a challenging yet
meaningful task and has been applied in many applications such as urban planning and …
meaningful task and has been applied in many applications such as urban planning and …
Ha-ccn: Hierarchical attention-based crowd counting network
VA Sindagi, VM Patel - IEEE Transactions on Image Processing, 2019 - ieeexplore.ieee.org
Single image-based crowd counting has recently witnessed increased focus, but many
leading methods are far from optimal, especially in highly congested scenes. In this paper …
leading methods are far from optimal, especially in highly congested scenes. In this paper …
Multi-level bottom-top and top-bottom feature fusion for crowd counting
VA Sindagi, VM Patel - Proceedings of the IEEE/CVF …, 2019 - openaccess.thecvf.com
Crowd counting presents enormous challenges in the form of large variation in scales within
images and across the dataset. These issues are further exacerbated in highly congested …
images and across the dataset. These issues are further exacerbated in highly congested …